And that's a good thing, because Python+NumPy syntax is far more cumbersome than either Julia or MATLAB's.
You can see this at a glance from this nice trilingual cheat sheet:
To those who regularly write Julia code, what is your workflow? The whole thing with Revise.jl did not suit me honestly. I have enjoyed programming in Rust orders of magnitude more because there's no run time and you can do AOT. My intention is not write scripts, but high performance numerical/scientific code, and with Julia's JIT-based design, rapid iteration (to me at least) feels slower than Rust (!).
End result: code that is uglier and still much slower than C++. Kind of a shame.
As a quick anecdote, in our take-home interview exercise, we usually receive answers in C++ or Julia, and the two fastest answers have been in Julia.
Of course it also depends on what additional libaries you are using, especially when it comes to parallel/GPU programming in C++, but easy to believe that Julia out of the box makes it easy to write high performance parallel software.
Yeah, I actually totally forgot to check the date...
Oh such a shame indeed! They didn’t even manage to produce better looking code at least?? Julia was looking great in 2019 but it was very buggy still so I stopped looking. Had hopes that by now it would be a good choice over C++ and Rust with similar performance.
I have always seen it as a potential alternative to Java, and definitely better than Python.
My experience working in it professionally was that it was... fine. But the GC in it was not good under load and not competitive with Java's.
It’s nothing like Google-the-ad-company influencing Chrome. The company consumes Julia for products to sell, rather. Maybe this affects the ordering of features landing, but… meh.
One could say that we can almost replicate the semantic of a C++ program, but writing in Julia. For example we can remove bounds checks in arrays or remove hidden memory allocations.
But the goal of a language for numerical computing is capturing the mathematical formulas using high level constructs closer to the original representation while compiling to efficient code.
Domain scientists want to play with the math and the formulas, not doing common subexpression elimination in their programs. Just curious to see how it evolves
Prelude of what's to come in the self-reinforcing cycle of machines talking to machines and drowning everything else.
